code: def featurize(self, tokenizer, feat_spec):
      if feat_spec.sep_token_extra:
          maybe_extra_sep = [tokenizer.sep_token]
          maybe_extra_sep_segment_id = [feat_spec.sequence_a_segment_id]
          special_tokens_count = 6  # CLS, SEP-SEP, SEP-SEP, SEP
      else:
          maybe_extra_sep = []
          maybe_extra_sep_segment_id = []
          special_tokens_count = 4  # CLS, SEP, SEP, SEP
  
      sentence1_tokens, sentence2_tokens = truncate_sequences(
          tokens_ls=[self.sentence1_tokens, self.sentence2_tokens],
          max_length=feat_spec.max_seq_length
          - len(self.word)
          - special_tokens_count,
      )
  
      unpadded_tokens = (
          self.word
          + [tokenizer.sep_token]
          + maybe_extra_sep
          + sentence1_tokens
          + [tokenizer.sep_token]
          + maybe_extra_sep
          + sentence2_tokens
          + [tokenizer.sep_token]
      )
      # Don't have a choice here -- just leave words as part of sent1
      unpadded_segment_ids = (
          [feat_spec.sequence_a_segment_id] * (len(self.word) + 1)
          + maybe_extra_sep_segment_id
          + [feat_spec.sequence_a_segment_id]
          * (len(sentence1_tokens) + 1)
          + maybe_extra_sep_segment_id
          + [feat_spec.sequence_b_segment_id]
          * (len(sentence2_tokens) + 1)
      )
  
      unpadded_inputs = add_cls_token(
          unpadded_tokens=unpadded_tokens,
          unpadded_segment_ids=unpadded_segment_ids,
          tokenizer=tokenizer,
          feat_spec=feat_spec,
      )
  
      input_set = create_input_set_from_tokens_and_segments(
          unpadded_tokens=unpadded_inputs.unpadded_tokens,
          unpadded_segment_ids=unpadded_inputs.unpadded_segment_ids,
          tokenizer=tokenizer,
          feat_spec=feat_spec,
      )
  
      word_sep_offset = 2 if feat_spec.sep_token_extra else 1
      sent1_sep_offset = 2 if feat_spec.sep_token_extra else 1
  
      # Both should be inclusive spans at the end
      sentence1_span = ExclusiveSpan(
          start=self.sentence1_span[0]
          + unpadded_inputs.cls_offset
          + word_sep_offset
          + len(self.word),
          end=self.sentence1_span[1]
          + unpadded_inputs.cls_offset
          + word_sep_offset
          + len(self.word),
      ).to_inclusive()
      sentence2_span = ExclusiveSpan(
          start=self.sentence2_span[0]
          + unpadded_inputs.cls_offset
          + word_sep_offset
          + sent1_sep_offset
          + len(self.word)
          + len(sentence1_tokens),
          end=self.sentence2_span[1]
          + unpadded_inputs.cls_offset
          + word_sep_offset
          + sent1_sep_offset
          + len(self.word)
          + len(sentence1_tokens),
      ).to_inclusive()
  
      return DataRow(
          guid=self.guid,
          input_ids=np.array(input_set.input_ids),
          input_mask=np.array(input_set.input_mask),
          segment_ids=np.array(input_set.segment_ids),
          spans=np.array([sentence1_span, sentence2_span]),
          label_id=self.label_id,
          tokens=unpadded_inputs.unpadded_tokens,
          word=self.word,
      )
  
imports: import numpy
  import torch
  from dataclasses import dataclass
  from typing import List
  import string
  from jiant.tasks.core import BaseExample, BaseTokenizedExample, BaseDataRow, BatchMixin, SuperGlueMixin, Task, TaskTypes
  from jiant.tasks.lib.templates.shared import labels_to_bimap, add_cls_token, create_input_set_from_tokens_and_segments
  from jiant.tasks.utils import truncate_sequences, ExclusiveSpan
  from jiant.utils.python.io import read_json_lines
  from jiant.utils import retokenize
  from jiant.utils.tokenization_normalization import normalize_tokenizations
label_desc: Use image node_id 1 for calling the TokenizedExample obj's underlying member method code with example usage: obj.featurize(tokenizer, feat_spec) and returns: DataRow